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The value of scientists engaging with community members and other public audiences is widely recognized, and there is a growing literature devoted to the theory and practice of public engagement with science. However, as a group of professionals concerned with how public engagement is understood and practiced in the fields of ecology and environmental science, we see a need for accessible guidance for scientists who want to engage effectively, and for scientific leaders who want to support successful public engagement programs in their institutions. Here, we highlight six attributes of successful public engagement efforts led by scientists and scientific institutions: (1) strategic, (2) cumulative, (3) reciprocal, (4) reflexive, (5) equitable, and (6) evidence‐based. By designing and developing practices that incorporate these attributes, scientists and scientific organizations will be better poised to build two‐way linkages with communities that, over time, support science‐informed decision‐making in society and societally informed decision‐making in science. 

Abstract Co‐production is becoming an increasingly important approach to facilitating integrated climate, environmental, social and earth systems research to achieve societal impact. Across the research and science‐policy ecosystem, there are multiple indicators of its growing prominence as means to engage the public and generate research that is more likely to address real‐world problems and community priorities. Power plays a key role in co‐production, as power imbalances can affect participation, decision‐making, and the distribution of benefits. Addressing power imbalances, through a focus on equity in co‐production, helps to ensure past harms are addressed and participants have the resources and opportunities to contribute effectively. This special issue includes articles that explore equity in co‐production. Articles describe the results of projects that used co‐production approaches, social scientific findings that can inform equitable co‐production, and some that do both. Across the submissions, inclusive decision‐making, strengthening capacity at multiple levels, and fostering trust and respect were key themes. The collection provides practical lessons and future directions to advance equity in co‐production processes, meeting the urgent demand for more inclusive and impactful environmental science practices. 

Hardware random number generators (HRNG) are widely used in the computer world for security purposes as well as in the science world as a source of the high-quality randomness for the models and simulations. Currently existing HRNG are either costly or very slow and of questionable quality. This work proposes a simple design of the HRNG based on the low-number photon absorption by a detector (a photo-multiplier tube of a silicon-based one i.e. SiPM, MPPC, etc.) that can provide a large volume of high-quality random numbers. The prototype design, different options of processing and the testing of quality of the generator output are presented. 

Abstract. Aerosol interactions with clouds represent a significant uncertainty in our understanding of the Earth system. Deep convective clouds may respond to aerosol perturbations in several ways that have proven difficult to elucidate with observations. Here, we leverage the two busiest maritime shipping lanes in the world, which emit aerosol particles and their precursors into an otherwise relatively clean tropical marine boundary layer, to make headway on the influence of aerosol on deep convective clouds. The recent 7-fold change in allowable fuel sulfur by the International Maritime Organization allows us to test the sensitivity of the lightning to changes in ship plume aerosol number-size distributions. We find that, across a range of atmospheric thermodynamic conditions, the previously documented enhancement of lightning over the shipping lanes has fallen by over 40 %. The enhancement is therefore at least partially aerosol-mediated, a conclusion that is supported by observations of droplet number at cloud base, which show a similar decline over the shipping lane. These results have fundamental implications for our understanding of aerosol–cloud interactions, suggesting that deep convective clouds are impacted by the aerosol number distribution in the remote marine environment. 

We predict the sensitivity of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) to faint, resolved Milky Way satellite galaxies and outer-halo star clusters. We characterize the expected sensitivity using simulated LSST data from the LSST Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) accessed and analyzed with the Rubin Science Platform as part of the Rubin Early Science Program. We simulate resolved stellar populations of Milky Way satellite galaxies and outer-halo star clusters over a wide range of sizes, luminosities, and heliocentric distances, which are broadly consistent with expectations for the Milky Way satellite system. We inject simulated stars into the DC2 catalog with realistic photometric uncertainties and star/galaxy separation derived from the DC2 data itself. We assess the probability that each simulated system would be detected by LSST using a conventional isochrone matched-filter technique. We find that assuming perfect star/galaxy separation enables the detection of resolved stellar systems with $M_V$= 0 mag and $r_{1/2}$= 10 pc with >50% efficiency out to a heliocentric distance of ~250 kpc. Similar detection efficiency is possible with a simple star/galaxy separation criterion based on measured quantities, although the false positive rate is higher due to leakage of background galaxies into the stellar sample. When assuming perfect star/galaxy classification and a model for the galaxy-halo connection fit to current data, we predict that 89 +/- 20 Milky Way satellite galaxies will be detectable with a simple matched-filter algorithm applied to the LSST wide-fast-deep data set. Different assumptions about the performance of star/galaxy classification efficiency can decrease this estimate by ~7-25%, which emphasizes the importance of high-quality star/galaxy separation for studies of the Milky Way satellite population with LSST.